{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "shape = (3,3)\n",
    "initializer = tf.initializers.he_normal()\n",
    "var = tf.Variable(initializer(shape=shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.8060966  -0.02901677  0.52493757]\n",
      " [ 0.09943676 -0.44153076 -0.08580101]\n",
      " [ 0.25343215 -0.10636837 -1.0344335 ]]\n"
     ]
    }
   ],
   "source": [
    "init = tf.global_variables_initializer()\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    print(sess.run(var))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
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    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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